Crowdsourcing

Many applications require input that can hardly be provided automatically, since the task is too complex for computers, but rather trivial for human beings. At the same time, the scale of these problems does not allow the task to be solved by dedicated experts; rather, the wisdom of the crowd is leveraged for data annotation, classification, or even data capturing. Managing the data that is provided by such crowdsourcing applications and guaranteeing a high degree of data quality (e.g., automatic disambiguation and integration into a coherent data set) is crucial.

Work at the DBIS group addresses the following applications:

Citizens' Observatories: While various dedicated sensors for a broad range of environmental parameters exist, usually together with specialized applications for automated data analysis, there are still many environmental phenomena that cannot be easily captured. An example is soil degradation and erosion. In other cases, even though sophisticated sensors exist, the sensor information can hardly be processed automatically, especially when is consists of or includes images, 3D models, etc. Examples are invasive alien species which can only be identified with the help of professional or lay taxonomists. In our work, we have devised a smartphone app (COSA: Citizens' Observatories Smartphone App) in collaboration with experts from the environmental sciences that supports the crowdsourcing of data capturing and/or data analysis in the environmental sciences.

Crowdsourced event detection and annotation in videos: The wisdom of the crowd is used to create metadata annotations to videos. We have used the contribution of crowd workers to annotate sports videos (e.g., in a soccer video: which player is in possession of the ball, on which part of the filed, etc.). These annotations then allow to support novel retrieval applications, called SportSense.

Querying crowdsourced multimedia data: Crowdsourcing multimedia content (e.g., images and videos of places of interest in tourist applications) leads to big but also heterogeneous collections. In our work, we aim at providing a large variety of query paradigms on top of crowdsourced and/or curated content, and support a range of different devices for accessing hese data.